Completing LOM - How Additional Axioms Increase the Utility of Learning Object Metadata

Learning Objects Metadata describing educational resources in order to allow better reusability and retrieval. Unfortunately, annotating complete courses thoroughly with LOM metadata can be a tedious task. In this paper we show how additional inference rules can make this task easier, and allows us to derive additional metadata from existing ones. Additionally, using these rules as integrity constraints helps us to define the constraints on LOM fields, thus taking an important step towards a complete axiomatization of LOM metadata (with the goal of transforming the LOM definitions from a simple syntactical description into a complete ontology). In this paper we will use RDF metadata descriptions and an inference language explicitly developed for RDF (TRIPLE) to represent metadata and axioms. We show how these rules can be applied for the extensions of course metadata, the creation of views onto the metadata or metadata consistency checking.

• Type: Conference
• Year: 2003
• Where: ICALT
• Authors: Jan Brase, Mark Painter, Wolfgang Nejdl
• Keywords: ICALT 2003, LOM Metadata, Machine Learning, Metadata, Objects Metadata